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ASYMMETRIES IN CONDITIONAL MEAN AND VARIANCE: MODELLING STOCK RETURNS BY asMA-asQGARCH

Listed author(s):
  • Brännäs, Kurt

    ()

    (Department of Economics, Umeå University)

  • de Gooijer, Jan G.

    ()

    (Department of Quantitative Economics)

The asymmetric moving average model (asMA) is extended to allow for asymmetric quadratic conditional heteroskedasticity (asQGARCH). The asymmetric parametrization of the conditional variance encompasses the quadratic GARCH model of Sentana (1995). We introduce a framework for testing asymmetries in the conditional mean and the conditional variance, separately or jointly. Some of the new model's moment properties are also derived. Empirical results are given for the daily returns of the composite index of the New York Stock Exchange. There is strong evidence of asymmetry in both the conditional mean and conditional variance functions. In a genuine out-of-sample forecasting experiment the performance of the best fitted asMA-asQGARCH model is compared to pure asMA and no-change forecasts. This is done both in terms of conditional mean forecasting as well in terms of risk forecasting.

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Paper provided by Umeå University, Department of Economics in its series Umeå Economic Studies with number 535.

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Length: 21 pages
Date of creation: 16 May 2000
Publication status: Published in Journal of Forecasting , 2004, pages 155-171.
Handle: RePEc:hhs:umnees:0535
Contact details of provider: Postal:
Department of Economics, Umeå University, S-901 87 Umeå, Sweden

Phone: 090 - 786 61 42
Fax: 090 - 77 23 02
Web page: http://www.econ.umu.se/
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  1. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  2. Brannas, K. & Ohlsson, H., 1995. "Asymmetric Cycles and Temporal Aggregation," Papers 1995-11, Uppsala - Working Paper Series.
  3. G. William Schwert, 1988. "Why Does Stock Market Volatility Change Over Time?," NBER Working Papers 2798, National Bureau of Economic Research, Inc.
  4. Lebaron, B., 1990. "Some Relations Between Volatility And Serial Correlations In Stock Market Returns," Working papers 9002, Wisconsin Madison - Social Systems.
  5. Lundbergh, Stefan & Teräsvirta, Timo, 1998. "Modelling economic high-frequency time series with STAR-STGARCH models," SSE/EFI Working Paper Series in Economics and Finance 291, Stockholm School of Economics.
  6. Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993. "On the relation between the expected value and the volatility of the nominal excess return on stocks," Staff Report 157, Federal Reserve Bank of Minneapolis.
  7. Sentana,E., 1995. "Quadratic Arch Models," Papers 9517, Centro de Estudios Monetarios Y Financieros-.
  8. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 1-42.
  9. Harvey, Campbell R. & Siddique, Akhtar, 1999. "Autoregressive Conditional Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(04), pages 465-487, December.
  10. Li, C W & Li, W K, 1996. "On a Double-Threshold Autoregressive Heteroscedastic Time Series Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(3), pages 253-274, May-June.
  11. Gregory Koutmos, 1999. "Asymmetric index stock returns: evidence from the G-7," Applied Economics Letters, Taylor & Francis Journals, vol. 6(12), pages 817-820.
  12. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
  13. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
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